diff --git a/Shayan1/Prod/nash/solver.py b/Shayan1/Prod/nash/solver.py
new file mode 100644
index 0000000..ce94e21
--- /dev/null
+++ b/Shayan1/Prod/nash/solver.py
@@ -0,0 +1,223 @@
+import itertools
+
+import numpy as np
+
+
+def find_pure_nash_equilibria(game):
+    """Brute-force search over all strategy profiles. Works for any number of players."""
+    equilibria = []
+    for profile in game.all_profiles():
+        if _is_pure_nash(game, profile):
+            equilibria.append(profile)
+    return equilibria
+
+
+def find_dominant_strategies(game):
+    """Find a strictly dominant strategy for each player, if one exists.
+
+    A strategy *s* for player *i* is **strictly dominant** when it yields a
+    strictly higher payoff than every other strategy of player *i* against
+    *all* possible opponent profiles.
+
+    Returns ``dict[int, int | None]`` mapping each player index to the index
+    of their strictly dominant strategy (or ``None`` when no such strategy
+    exists).  Works for any number of players.
+    """
+    counts = game.strategy_counts()
+    result = {}
+    for i in range(game.num_players):
+        if counts[i] < 2:
+            result[i] = None
+            continue
+        dominant = None
+        for s in range(counts[i]):
+            is_dominant = True
+            for other in range(counts[i]):
+                if other == s:
+                    continue
+                # Compare s vs other across every opponent profile.
+                opp_ranges = [range(n) for j, n in enumerate(counts) if j != i]
+                for opp_profile in itertools.product(*opp_ranges):
+                    profile_s = opp_profile[:i] + (s,) + opp_profile[i:]
+                    profile_other = opp_profile[:i] + (other,) + opp_profile[i:]
+                    if game.payoff(i, profile_s) <= game.payoff(i, profile_other):
+                        is_dominant = False
+                        break
+                if not is_dominant:
+                    break
+            if is_dominant:
+                dominant = s
+                break
+        result[i] = dominant
+    return result
+
+
+def _is_pure_nash(game, profile):
+    strategy_counts = game.strategy_counts()
+    for i in range(game.num_players):
+        current_payoff = game.payoff(i, profile)
+        for s in range(strategy_counts[i]):
+            if s == profile[i]:
+                continue
+            deviation = list(profile)
+            deviation[i] = s
+            if game.payoff(i, tuple(deviation)) > current_payoff:
+                return False
+    return True
+
+
+def find_mixed_nash_equilibria(game, tol=1e-7):
+    """Support enumeration for 2-player games (assumes non-degenerate games, i.e. equal-size
+    supports). Returns list of (x, y) probability vectors.
+    """
+    if game.num_players != 2:
+        raise NotImplementedError(
+            "Mixed Nash equilibrium solving is only implemented for 2-player games"
+        )
+
+    A, B = game.payoffs
+    m, n = A.shape
+    equilibria = []
+
+    for k in range(1, min(m, n) + 1):
+        for support_i in itertools.combinations(range(m), k):
+            for support_j in itertools.combinations(range(n), k):
+                y, v1 = _solve_indifference(A, support_i, support_j, tol)
+                x, v2 = _solve_indifference(B.T, support_j, support_i, tol)
+                if y is None or x is None:
+                    continue
+                if _is_feasible(A, B, support_i, support_j, x, y, v1, v2, tol):
+                    if not _already_found(equilibria, x, y, tol):
+                        equilibria.append((x, y))
+
+    return equilibria
+
+
+def _solve_support(A, B, support_i, support_j, tol):
+    y_result = _solve_indifference(A, support_i, support_j, tol)
+    x_result = _solve_indifference(B.T, support_j, support_i, tol)
+    if y_result is None or x_result is None:
+        return None, None
+    y, v1 = y_result
+    x, v2 = x_result
+    return x, y
+
+
+def _solve_indifference(matrix, eq_indices, var_indices, tol):
+    """Solve for a probability distribution over var_indices that makes the player whose
+    payoff matrix is ``matrix`` indifferent among the strategies in ``eq_indices``.
+    """
+    k = len(eq_indices)
+    # Build the linear system: for each eq_index except the last, the difference
+    # in expected payoffs between that index and the last must be zero.
+    if k == 1:
+        probs = np.zeros(matrix.shape[1])
+        probs[list(var_indices)] = 1.0
+        value = matrix[eq_indices[0], var_indices[0]]
+        if value < -tol:
+            return None, None
+        return np.clip(probs, 0, None), value
+
+    A_mat = np.zeros((k, len(var_indices)))
+    b_vec = np.zeros(k)
+    last_row = matrix[eq_indices[-1], list(var_indices)]
+    for row_i in range(k - 1):
+        this_row = matrix[eq_indices[row_i], list(var_indices)]
+        A_mat[row_i] = this_row - last_row
+        b_vec[row_i] = 0.0
+    A_mat[-1] = 1.0
+    b_vec[-1] = 1.0
+
+    try:
+        probs_support = np.linalg.solve(A_mat, b_vec)
+    except np.linalg.LinAlgError:
+        return None, None
+
+    # Check for negative probabilities in the solved support
+    if any(p < -tol for p in probs_support):
+        return None, None
+    probs = np.zeros(matrix.shape[1])
+    for idx, v in enumerate(var_indices):
+        probs[v] = probs_support[idx]
+    probs = np.clip(probs, 0, None)
+    if np.sum(probs[list(var_indices)]) < tol:
+        return None, None
+    probs[list(var_indices)] /= np.sum(probs[list(var_indices)])
+    value = matrix[eq_indices[0]] @ probs
+    return np.clip(probs, 0, None), value
+
+
+def _is_feasible(A, B, support_i, support_j, x, y, v1, v2, tol):
+    m, n = A.shape
+    # All probabilities non-negative
+    if any(x[i] < -tol for i in support_i) or any(y[j] < -tol for j in support_j):
+        return False
+    # Best-response check: no strategy outside support gives higher payoff
+    for i in range(m):
+        if i not in support_i and A[i] @ y > v1 + tol:
+            return False
+    for j in range(n):
+        if j not in support_j and B[:, j] @ x > v2 + tol:
+            return False
+    return True
+
+
+def _already_found(equilibria, x, y, tol):
+    for ex, ey in equilibria:
+        if np.allclose(x, ex, atol=tol) and np.allclose(y, ey, atol=tol):
+            return True
+    return False
+
+
+def reduce_game(game):
+    """Iteratively eliminate strictly dominated strategies for each player.
+
+    Returns a tuple of (reduced_game, logs).
+    """
+    current_game = game
+    logs = []
+    while True:
+        eliminated_any = False
+        counts = current_game.strategy_counts()
+        for i in range(current_game.num_players):
+            if counts[i] < 2:
+                continue
+            dominated_idx = None
+            for s in range(counts[i]):
+                for other in range(counts[i]):
+                    if other == s:
+                        continue
+                    is_dominated = True
+                    opp_ranges = [range(n) for j, n in enumerate(counts) if j != i]
+                    for opp_profile in itertools.product(*opp_ranges):
+                        profile_s = opp_profile[:i] + (s,) + opp_profile[i:]
+                        profile_other = opp_profile[:i] + (other,) + opp_profile[i:]
+                        if current_game.payoff(i, profile_other) <= current_game.payoff(i, profile_s):
+                            is_dominated = False
+                            break
+                    if is_dominated:
+                        dominated_idx = s
+                        break
+                if dominated_idx is not None:
+                    break
+
+            if dominated_idx is not None:
+                player_name = current_game.players[i]
+                strategy_name = current_game.strategies[i][dominated_idx]
+                logs.append(f"Player '{player_name}' eliminated dominated strategy '{strategy_name}'")
+                new_players = current_game.players
+                new_strategies = []
+                for j, strats in enumerate(current_game.strategies):
+                    if j == i:
+                        new_strategies.append(strats[:dominated_idx] + strats[dominated_idx+1:])
+                    else:
+                        new_strategies.append(list(strats))
+                new_payoffs = []
+                for payoff in current_game.payoffs:
+                    new_payoffs.append(np.delete(payoff, dominated_idx, axis=i))
+                current_game = current_game.__class__(new_players, new_strategies, new_payoffs)
+                eliminated_any = True
+                break
+        if not eliminated_any:
+            break
+    return current_game, logs